Effect of excipient particle size distribution variability on compact tensile strength; and its in-line prediction by force-displacement and force-time profiling.

Background: Direct compression is potentially sensitive to particle size distribution (PSD) variability in pharmaceutical grade excipients. Yet, the impact is insufficiently studied. Furthermore, the use of force sensor as a process analytical technology (PAT) platform, to monitor the effect of PSD variations on compact tensile strength, is a readily available but underutilized strategy.

Methods: To address these shortfalls, the effect of PSD variability on compaction was investigated. Low (4% w/w drug) and high (15% w/w drug) dose blends comprising chlorpheniramine, microcrystalline cellulose and spray-agglomerated lactose were tableted. The PSD of spray-agglomerated lactose was varied by adding ungranulated fines to simulate commercially-relevant variability. Tensile strength and disintegration time were determined. The use of force sensor, to generate force-displacement and force-time profiles, for in-line tensile strength prediction was evaluated.

Results: Increasing proportion of ungranulated fines (≥ 16%) reduced tensile strength by 10% and 4% in low and high dose formulations (p < 0.02). Increased friction during compaction hindered particle movement and reduced the energy available for bonding. Nevertheless, disintegration performances were equally acceptable for immediate drug release (≈ 30 s). Modelling of tensile strength with force-displacement and force-time profiles yielded ≥ 98% accuracy for in-line prediction (relative root mean square error of prediction = 3.7% and 4.8%).

Conclusion: A better understanding of the relationship between PSD variability and direct compression was attained; and force-displacement and force-time profiling are pragmatic and elegant PAT strategies. Significantly, with further refinements, the force sensor in the rotary tablet press can be repurposed for process monitoring and quality inspection. This unlocks opportunities for process understanding and control, without additional investments in PAT platforms.